<p>This text takes an engineering approach to the use of algorithms on multiprocessor machines. Almost half of the text is devoted to matrix computations. Coverage of hypercube and alliant architectures, the two most widely used supercomputers, is included.</p>

Analysis and Design of Parallel Algorithms: Arithmetic and Matrix Problems (MCGRAW HILL SERIES IN SUPERCOMPUTING AND PARALLEL PROCESSING)

Computers & Technology

Lakshmivarahan, S.

Dhall, Sudarshan K.

Programming

Mcgraw-Hill College

Analysis and Design of Parallel Algorithms/Solution Manual

Reference

McGraw=Hill

The prefix operation on a set of data is one of the simplest and most useful building blocks in parallel algorithms. This introduction to those aspects of parallel programming and parallel algorithms that relate to the prefix problem emphasizes its use in a broad range of familiar and important problems. The book illustrates how the prefix operation approach to parallel computing leads to fast and efficient solutions to many different kinds of problems. Students, teachers, programmers, and computer scientists will want to read this clear exposition of an important approach.

Parallel Computing Using the Prefix Problem

Oxford University Press

<p>Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes computational techniques. Parts IV to VII are concerned with how statistical and dynamic ideas can be incorporated into the classical framework. Key themes covered here include estimation theory, stochastic and dynamic models, and sequential filtering. The final part addresses the predictability of dynamical systems. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints.</p>

Dynamic Data Assimilation: A Least Squares Approach (Encyclopedia of Mathematics and its Applications, Series Number 104)

Lewis, John M.

Dhall, Sudarshan

Encyclopedias & Subject Guides

Cambridge University Press

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